There is an intense interest in the possibility that neighborhood characteristics influence active transportation such as walking or biking. The purpose of this paper is to illustrate how a spatial cluster identification method can evaluate the geographic variation of active transportation and identify neighborhoods with unusually high/low levels of active transportation.
Self-reported walking/biking prevalence, demographic characteristics, street connectivity variables, and neighborhood socioeconomic data were collected from respondents to the 2001 California Health Interview Survey (CHIS; N=10,688) in Los Angeles County (LAC) and San Diego County (SDC). Spatial scan statistics were used to identify clusters of high or low prevalence (with and without age-adjustment) and the quantity of time spent walking and biking. The data, a subset from the 2001 CHIS, were analyzed in 2007-2008.
Geographic clusters of significantly high or low prevalence of walking and biking were detected in LAC and SDC. Structural variables such as street connectivity and shorter block lengths are consistently associated with higher levels of active transportation, but associations between active transportation and socioeconomic variables at the individual and neighborhood levels are mixed. Only one cluster with less time spent walking and biking among walkers/bikers was detected in LAC, and this was of borderline significance. Age-adjustment affects the clustering pattern of walking/biking prevalence in LAC, but not in SDC.
The use of spatial scan statistics to identify significant clustering of health behaviors such as active transportation adds to the more traditional regression analysis that examines associations between behavior and environmental factors by identifying specific geographic areas with unusual levels of the behavior independent of predefined administrative units.
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"Even though STSS-based approaches have commonly been used in epidemiology to detect disease outbreaks (Kulldorff et al. 2005; Neill et al. 2005), in crime science to detect crime hotspots (Maciejewski et al. 2010; Nakaya and Yano 2010) amongst others (SaTScan 2010); their investigation in transportation science is a recent research endeavour. According to the best of our knowledge, only Huang et al. (2009) investigated the use of spatial scan statistics to detect clusters of active transportation (i.e. walking, cycling); however, they have not considered the temporal aspect of the phenomenon. "
[Show abstract][Hide abstract]ABSTRACT: This paper proposes two novel methods for non-recurrent congestion (NRC) event detection on heterogeneous urban road networks based on link journey time (LJT) estimates. Heterogeneity exists on urban road networks in two main aspects: variation in link lengths and data quality. The proposed NRC detection methods are referred to as percentile-based NRC detection and space–time scan statistics (STSS) based NRC detection. Both of these methods capture the heterogeneity of an urban road network by modelling the LJTs with a lognormal distribution. Empirical analyses are conducted on London's urban road network consisting of 424 links for the 20 weekdays of October 2010. Various parameter settings are tested for both of the methods, and the results favour STSS-based NRC detection method over the percentile-based NRC detection method. Link-based analyses demonstrate the effectiveness of the proposed methods in capturing the heterogeneity of the analysed road network.
"For example, respondent home addresses from many of the major US health surveys such as Add Health, the California Health Interview Survey (CHIS), the National Health and Nutrition Examination Survey (NHANES) and cohort studies , such as the Nurses' Health Study (NHS), the Multi- Ethnic Study of Atherosclerosis (MESA), the NCI- American Association of Retired Persons (AARP) cohort, the Women's Health Initiative and the Los Angeles Family and Neighborhood Survey, have been geocoded and many geospatial analyses are complete or in progress (e.g. CHIS, Huang et al. 2009; NHANES, Wen and Kowaleski-Jones 2012; NHS, James et al. 2013; MESA, Hirsch et al. 2014; NCI-AARP, Major et al. 2010; WHI, Kerr et al. 2014). This exciting and important advance reflects the fact that the idea of spatial and contextual approaches has captured the imagination of chronic disease and health behaviour researchers. "
[Show abstract][Hide abstract]ABSTRACT: In the past 15 years, a major research enterprise has emerged that is aimed at understanding associations between geographic and contextual features of the environment (especially the built environment) and elements of human energy balance, including diet, weight and physical activity. Here we highlight aspects of this research area with a particular focus on research and opportunities in the United States as an example. We address four main areas: (1) the importance of valid and comparable data concerning behaviour across geographies; (2) the ongoing need to identify and explore new environmental variables; (3) the challenge of identifying the causally relevant context; and (4) the pressing need for stronger study designs and analytical methods. Additionally, we discuss existing sources of geo-referenced health data which might be exploited by interdisciplinary research teams, personnel challenges and some aspects of funding for geospatial research by the US National Institutes of Health in the past decade, including funding for international collaboration and training opportunities.
"Another study, using data from the Behavioral Risk Factor Surveillance System (BRFSS) from 2000–2006, showed higher physical activity clusters in parts of the San Francisco Bay Area, northwest coastal states (Washington and Oregon), and by Lake Michigan . Collectively, the results from these recent U.S. studies [31,36] , earlier studies in Australia, which indicated a positive influence of coastal areas on physical activity [55,56], and the present study, suggest that living near large bodies of water has a positive relationship with physical activity. However, since all of this evidence is from cross-sectional studies, the direction of these effects cannot be determined. "
[Show abstract][Hide abstract]ABSTRACT: Identifying spatial clusters of chronic diseases has been conducted over the past several decades. More recently these approaches have been applied to physical activity and obesity. However, few studies have investigated built environment characteristics in relation to these spatial clusters. This study's aims were to detect spatial clusters of physical activity and obesity, examine whether the geographic distribution of covariates affects clusters, and compare built environment characteristics inside and outside clusters.
In 2004, Nurses' Health Study participants from California, Massachusetts, and Pennsylvania completed survey items on physical activity (N = 22,599) and weight-status (N = 19,448). The spatial scan statistic was utilized to detect spatial clustering of higher and lower likelihood of obesity and meeting physical activity recommendations via walking. Clustering analyses and tests that adjusted for socio-demographic and health-related variables were conducted. Neighborhood built environment characteristics for participants inside and outside spatial clusters were compared.
Seven clusters of physical activity were identified in California and Massachusetts. Two clusters of obesity were identified in Pennsylvania. Overall, adjusting for socio-demographic and health-related covariates had little effect on the size or location of clusters in the three states with a few exceptions. For instance, adjusting for husband's education fully accounted for physical activity clusters in California. In California and Massachusetts, population density, intersection density, and diversity and density of facilities in two higher physical activity clusters were significantly greater than in neighborhoods outside of clusters. In contrast, in two other higher physical activity clusters in California and Massachusetts, population density, diversity of facilities, and density of facilities were significantly lower than in areas outside of clusters. In Pennsylvania, population density, intersection density, diversity of facilities, and certain types of facility density inside obesity clusters were significantly lower compared to areas outside the clusters.
Spatial clustering techniques can identify high and low risk areas for physical activity and obesity. Although covariates significantly differed inside and outside the clusters, patterns of differences were mostly inconsistent. The findings from these spatial analyses could eventually facilitate the design and implementation of more resource-efficient, geographically targeted interventions for both physical activity and obesity.
Full-text · Article · Dec 2014 · BMC Public Health